Most of the state-of-the-art 2D [1]}, [2]} and 3D [3]}, [4]}, [5]}, [6]} object detection methods produce a single box with a probability score for each detection. While this probability encompasses the existence and semantic uncertainty, it does not accurately measure the localization uncertainty. Recently, [7]} proposed to explicitly predict the intersection-over-union (IoU) of each 2D detection with the ground truth label. They use the predicted IoU to refine and score the detections. Estimating the IoU provides a measure of the detection's localization error. In this paper, we estimate a probability distribution over box detections instead of only the mean, and the variance of the predicted distribution indicates the amount of uncertainty in the position of the box corners.
